.loom file format

Introduction

The .loom file format is designed to efficiently hold large omics datasets. Typically, such data takes the form of a large matrix of numbers, along with metadata for the rows and columns. For example, single-cell RNA-seq data consists of expression measurements for all genes (rows) in a large number of cells (columns), along with metadata for genes (e.g. Chromosome, Strand, Location, Name), and for cells (e.g. Species, Sex, Strain, GFP positive).

We designed .loom files to represent such datasets in a way that treats rows and columns the same. You may want to cluster both genes and cells, you may want to perform PCA on both of them, and filter based on quality controls. SQL databases and other data storage solutions almost always treat data as a table, not a matrix, and makes it very hard to add arbitrary metadata to rows and columns. In contrast, .loom makes this very easy.

Furthermore, current and future datasets can have tens of thousands of rows (genes) and hundreds of thousands of columns (cells). We designed .loom for efficient access to arbitrary rows and columns.

The annotated matrix format lends itself to very natural representation of common analysis tasks. For example, the result of a clustering algorithm can be stored simply as another attribute that gives the cluster ID for each cell. Dimensionality reduction such as PCA or t-SNE, similarly, can be stored as two attributes giving the projection coordinates of each cell.

Finally, we recognize the importance of graph-based analyses of such datasets. Loom supports graphs of both the rows (e.g. genes) and the columns (e.g. cells), and multiple graphs can be stored each file.

Specification

A valid .loom file conforms to the following:

  • There MUST be a single dataset at /matrix
  • There can OPTIONALLY be a subgroup /layers containing additional matrices (called “layers”)
  • Each additional layer MUST have the same (N, M) shape
  • Each layer can have a different data type, compression, chunking etc.
  • There can OPTIONALLY be at least one HDF5 attribute on the root / group, which MUST be of type string and should be interpreted as attributes of the whole .loom file. The following HDF5 attributes are standard:
  • title, a short title for the dataset
  • description, a longer description of the dataset
  • url, a link to a web page for the dataset
  • doi, a DOI for the paper where the dataset was published
  • There MUST be a group /row_attrs
  • There can OPTIONALLY be one or more datasets at /row_attrs/{name} of length N and type float64, int or string
  • There MUST be a group /col_attrs
  • There can OPTIONALLY be one or more datasets at /col_attrs/{name} of length M and type float64, int or string

The datasets under /row_attrs should be semantically interpreted as row attributes, with one value per row of the main matrix, and in the same order. Therefore, all datasets under this group must be one-dimensional arrays with exactly N elements, where N is the number of rows in the main matrix.

The datasets under /col_attrs should be semantically interpreted as column attributes, with one value per column of the main matrix, and in the same order. Therefore, all datasets under this group must be one-dimensional arrays with exactly M elements, where M is the number of columns in the main matrix.

As noted above, only three datatypes are allowedfor attributes; float64, int or string.

HDF5 concepts

The .loom format is based on HDF5, a standard for storing large numerical datasets. Quoting from h5py.org:

An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The most fundamental thing to remember when using h5py is: Groups work like dictionaries, and datasets work like NumPy arrays.

A valid .loom file is simply an HDF5 file that contains specific groups representing the main matrix as well as row and column attributes. Because of this, .loom files can be created and read by any language that supports HDF5, including Python, R, MATLAB, Mathematica, C, C++, Java, and Ruby.

Example

Here’s an example of the structure of a valid .loom file:

Group Type Description
/matrix float32[N,M] or uint16[N,M] Main matrix of N rows and M columns
/layers/ (subgroup) Subgroup of additional matrix layers
/row_attrs/ (subgroup) Subgroup of all row attributes
/row_attrs/Name string[N] Row attribute “Name” of type string
/col_attrs/ (subgroup) Subgroup of all column attributes
/col_attrs/CellID float64[M] Column attribute “CellID” of type float64